[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3278":3,"related-3278":82},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":81},3278,"Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10661-026-15959-x","Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring。Environmental Monitoring and Assessment","基于地物高光谱数据与机器学习的稻叶铅含量反演最优建模路径及跨尺度遥感监测研究。环境监测与评估",null,"Environmental Monitoring and Assessment","2026-09-22T00:00:00Z","论文",10,false,64,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,16,13,9,1,"基于地物高光谱与机器学习的水稻叶片铅含量反演建模研究，方法有创新但属细分领域学术进展，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业遥感","水稻","机器学习","高光谱遥感",[33,34],"水稻叶片 铅含量 高光谱 反演","稻米 重金属 遥感 监测","水稻叶片铅含量高光谱反演-3278",0,"10.1007\u002Fs10661-026-15959-x",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":9,"card":74,"direction":78,"ingested_from":80},"W7214027889",[41,44,47,50,53,56,58,60,63,66,68,70,72],{"name":42,"orcid":43},"Zhenlong Zhang","https:\u002F\u002Forcid.org\u002F0009-0008-2354-9123",{"name":45,"orcid":46},"Zhe Wang","https:\u002F\u002Forcid.org\u002F0000-0003-1266-7251",{"name":48,"orcid":49},"Chengxia Wang","https:\u002F\u002Forcid.org\u002F0009-0001-0820-2462",{"name":51,"orcid":52},"Wenxue Lin","https:\u002F\u002Forcid.org\u002F0000-0002-8245-9063",{"name":54,"orcid":55},"Jingyan Zhang","https:\u002F\u002Forcid.org\u002F0009-0004-4567-5316",{"name":57,"orcid":9},"Ying Luo",{"name":59,"orcid":9},"Jiaqian Zhang",{"name":61,"orcid":62},"Kai Ye","https:\u002F\u002Forcid.org\u002F0000-0002-2851-6741",{"name":64,"orcid":65},"Yiming Chen","https:\u002F\u002Forcid.org\u002F0000-0002-8121-3109",{"name":67,"orcid":9},"Chaoliang Peng",{"name":69,"orcid":9},"Duan Tian",{"name":71,"orcid":9},"Weihao Wang",{"name":73,"orcid":9},"Jiaxin Liu",{"tldr":75,"method":76,"finding":77,"direction":78,"opportunity":79},"研究基于地面高光谱与机器学习反演水稻叶片铅含量，并探索跨尺度遥感监测的最优建模路径。","地面高光谱数据结合机器学习建模，开展跨尺度遥感监测。","明确了水稻叶片铅含量反演的最优建模路径，实现跨尺度遥感监测。","农业遥感与作物表型","可探索多尺度遥感数据融合与迁移学习，提升重金属胁迫反演的普适性与精度。","openalex","2026-09-23T23:30:19.291361Z",{"total":83,"page":22,"page_size":83,"items":84},6,[85,120,164,217,250,293],{"id":86,"title":87,"url":88,"summary":89,"summary_zh":9,"content":9,"source_name":90,"source_url":88,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":91,"score_detail":92,"sources":96,"tags":98,"search_phrases":101,"slug":104,"view_count":36,"doi":105,"paper":106,"created_at":119},3135,"Non-destructive nitrogen estimation in pastures from UAV multispectral imagery using a heterogeneity-driven machine-learning framework","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10453-3","Non-destructive nitrogen estimation in pastures from UAV multispectral imagery using a heterogeneity-driven machine-learning framework。Precision Agriculture","Precision Agriculture",82,{"impact":18,"substance":93,"depth":18,"authority":94,"freshness":13,"relevant":22,"comment":95},22,14,"方法新颖、数据可靠，对草地精准施肥有参考价值，但属细分领域研究，未达重大突破层级。",[97],{"name":90,"url":88},[27,99,28,30,100],"无人机","草地氮素",[102,103],"UAV 多光谱 草地 氮素","Precision Agriculture 氮素估算","UAV多光谱草地氮素-3135","10.1007\u002Fs11119-026-10453-3",{"doi":105,"openalex_id":107,"authors":108,"venue":90,"cited_by_count":36,"oa_url":9,"card":9,"direction":9,"ingested_from":80},"W7213942551",[109,111,114,117],{"name":110,"orcid":9},"Antônio de Oliveira Costa Neto",{"name":112,"orcid":113},"Yiannis Ampatzidis","https:\u002F\u002Forcid.org\u002F0000-0002-3660-3298",{"name":115,"orcid":116},"Andrea Lazzari","https:\u002F\u002Forcid.org\u002F0000-0002-1521-6942",{"name":118,"orcid":9},"Jim Fletcher","2026-09-22T23:30:03.351447Z",{"id":121,"title":122,"url":123,"summary":124,"summary_zh":125,"content":9,"source_name":126,"source_url":123,"published_at":127,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":128,"score_detail":129,"sources":131,"tags":133,"search_phrases":135,"slug":138,"view_count":36,"doi":139,"paper":140,"created_at":163},2663,"Research on an automated mapping method for rice aboveground biomass based on low-altitude remote sensing","https:\u002F\u002Fdoi.org\u002F10.4081\u002Fjae.2026.2061","Accurate monitoring of rice aboveground biomass (AGB) is crucial for guiding agricultural production management. This study focuses on high-precision estimation and automated mapping of rice AGB. Field experiments were conducted in Nanxun District, Huzhou City, Zhejiang Province. We collected UAV RGB and multispectral images, rice AGB, and plant height data. By integrating vegetation indices, texture features, and plant height information, the AGB estimation model was established using algorithms such as Stacking. A framework combining \"SAM + MobileNetV3-Small classification\" was proposed to achieve automated paddy field extraction and phenology recognition. The results demonstrate that the rice AGB prediction model based on the Stacking ensemble algorithm performed excellently. The introduction of plant height significantly improved model accuracy. For example, during the heading stage, R2 increased from 0.421 to 0.739, and RPIQ rose from 1.995 to 3.015. The automated paddy field extraction and phenology recognition framework developed in this study achieved a segmentation accuracy of 0.968 and a classification accuracy of 0.993 on the dataset used in this study, without requiring manual annotation. This research provides a technical reference for automated and high-precision mapping of rice AGB.","准确监测水稻地上生物量(AGB)对指导农业生产管理至关重要。本研究聚焦水稻AGB的高精度估算与自动化制图。田间试验在浙江省湖州市南浔区开展，采集了无人机RGB和多光谱影像、水稻AGB及株高数据。通过融合植被指数、纹理特征和株高信息，利用Stacking等算法构建AGB估算模型。提出了一种“SAM+MobileNetV3-Small分类”框架，以实现稻田自动化提取和物候识别。结果表明，基于Stacking集成算法的水稻AGB预测模型表现优异，株高的引入显著提高了模型精度。例如，在抽穗期，R2从0.421提升至0.739，RPIQ从1.995提升至3.015。本研究开发的稻田自动化提取与物候识别框架在本研究数据集上实现了0.968的分割精度和0.993的分类精度，且无需人工标注。本研究为水稻AGB的自动化高精度制图提供了技术参考。","Journal of Agricultural Engineering","2026-09-15T00:00:00Z",78,{"impact":19,"substance":93,"depth":18,"authority":94,"freshness":17,"relevant":22,"comment":130},"基于无人机遥感与Stacking集成模型实现水稻地上生物量自动化制图，方法新颖、数据扎实，对精准农业管理有参考价值。",[132],{"name":126,"url":123},[27,99,28,29,134],"作物表型",[136,137],"作物表型 农业遥感 智慧农业 无人机","作物表型 农业遥感","作物表型农业遥感智慧农业无人机-2663","10.4081\u002Fjae.2026.2061",{"doi":139,"openalex_id":141,"authors":142,"venue":126,"cited_by_count":36,"oa_url":123,"card":158,"direction":78,"ingested_from":80},"W7213229220",[143,145,147,149,151,154,156],{"name":144,"orcid":9},"Honggang Xu",{"name":146,"orcid":9},"Xuehan Li",{"name":148,"orcid":9},"Jia Shen",{"name":150,"orcid":9},"Ziyi Li",{"name":152,"orcid":153},"Zhe Li","https:\u002F\u002Forcid.org\u002F0009-0008-6496-3697",{"name":155,"orcid":9},"Yiming Li",{"name":157,"orcid":9},"Pengcheng Nie",{"tldr":159,"method":160,"finding":161,"direction":78,"opportunity":162},"基于无人机RGB与多光谱影像，结合株高与Stacking集成算法，实现水稻地上生物量高精度自动制图。","无人机RGB\u002F多光谱影像、植被指数、纹理与株高，Stacking集成及SAM+M","引入株高显著提升精度，抽穗期R²从0.421升至0.739；自动稻田提取与物候识别精度达0.968和","可探索多生育期、多品种下株高与纹理特征的迁移性，并耦合深度学习实现全自动生物量时空制图。","2026-09-16T23:30:28.929376Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":9,"source_name":170,"source_url":167,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":172,"score_detail":173,"sources":178,"tags":180,"search_phrases":183,"slug":186,"view_count":36,"doi":187,"paper":188,"created_at":216},1898,"Connecting earth observation anomalies to farmer surveys for monitoring impacts of agricultural drought on rainfed rice yields in Nigeria","https:\u002F\u002Fdoi.org\u002F10.5194\u002Feo-1-77-2026","Abstract. Agricultural drought threatens rainfed rice production in Nigeria, where smallholder farmers depend on rainfall and have limited capacity to buffer climate shocks. While meteorological drought indices such as the SPI and the SPEI are widely used in national early warning systems, their ability to capture the impacts of droughts on rainfed rice yields at the smallholder field-level remains uncertain. This study evaluates the added value of earth observation (EO)-derived vegetation and soil moisture anomalies for monitoring and predicting drought impacts on rainfed rice yields in Nigeria. Satellite-based Normalized Difference Vegetation Index anomalies (NDVIA) and Soil Water Index anomalies (SWIA) were derived using a zonal clustering and thresholding approach and combined with farmer survey and yield data collected from 146 rainfed rice farmers across four major rice-growing states between 2019 and 2024. Multivariate regression models were used to assess the relationships between EO indicator anomalies and annual yield changes, and the effects of different zonal clustering and anomaly thresholds on anomaly calculation were evaluated. Results show that SPI and SPEI explain a substantial share of yield variability in some years, particularly when droughts coincide with sensitive phenological stages. However, EO-based anomaly indicators, especially SWIA (maximum improved R2 = 0.25), provide complementary information and significantly improve yield predictions in years when meteorological indices alone perform poorly. The timing of anomalies relative to rice phenology was critical, with droughts during panicle initiation having the largest yield impacts. Integrating EO-based vegetation and soil moisture anomaly indicators with existing meteorological indices can contribute to the monitoring of agricultural droughts and improve the operational relevance of early warning systems for rainfed rice farmers in Nigeria.","摘要：农业干旱威胁着尼日利亚依赖雨养的水稻生产，当地小农户依靠降雨为生，缓冲气候冲击的能力有限。尽管诸如标准化降水指数（SPI）和标准化降水蒸散指数（SPEI）等气象干旱指数被广泛应用于国家早期预警系统，但它们在捕捉小农田间尺度上干旱对雨养水稻产量影响的能力仍不确定。本研究评估了基于地球观测（EO）的植被和土壤湿度异常在监测和预测尼日利亚干旱对雨养水稻产量影响方面的附加价值。采用分区聚类和阈值方法，推导出基于卫星的归一化差异植被指数异常（NDVIA）和土壤水分指数异常（SWIA），并结合2019年至2024年间从四个主要水稻种植州的146位雨养水稻农户收集的农户调查和产量数据。使用多元回归模型评估EO指标异常与年度产量变化之间的关系，并评估不同分区聚类和异常阈值对异常计算的影响。结果表明，在某些年份，SPI和SPEI解释了产量变异的很大一部分，特别是当干旱与敏感物候阶段重合时。然而，基于EO的异常指标，尤其是SWIA（最大改进R²=0.25），提供了补充信息，并在气象指数单独表现不佳的年份显著改善了产量预测。异常相对于水稻物候的时机至关重要，穗分化期发生的干旱对产量影响最大。将基于EO的植被和土壤湿度异常指标与现有气象指数相结合，有助于监测农业干旱，并提高尼日利亚雨养水稻农户早期预警系统的业务相关性。","Earth Observation","2026-09-07T00:00:00Z",74,{"impact":174,"substance":93,"depth":18,"authority":175,"freshness":176,"relevant":22,"comment":177},15,12,7,"研究结合遥感与农户调查，提升雨养水稻干旱监测精度，对非洲小农预警有参考价值。",[179],{"name":170,"url":167},[27,28,29,181,182],"干旱监测","尼日利亚",[184,185],"农业遥感 尼日利亚 干旱监测 智慧农业","农业遥感 尼日利亚","农业遥感尼日利亚干旱监测智慧农业-1898","10.5194\u002Feo-1-77-2026",{"doi":187,"openalex_id":189,"authors":190,"venue":170,"cited_by_count":36,"oa_url":167,"card":211,"direction":78,"ingested_from":80},"W7162756810",[191,194,196,199,201,204,207,209],{"name":192,"orcid":193},"Nick Gutkin","https:\u002F\u002Forcid.org\u002F0000-0003-4708-3483",{"name":195,"orcid":9},"Chiamaka I. Ehiemere",{"name":197,"orcid":198},"Koen De Vos","https:\u002F\u002Forcid.org\u002F0000-0002-4607-7877",{"name":200,"orcid":9},"Nnamdi Ehiemere",{"name":202,"orcid":203},"Jeroen Degerickx","https:\u002F\u002Forcid.org\u002F0000-0001-9022-9623",{"name":205,"orcid":206},"Sarah Gebruers","https:\u002F\u002Forcid.org\u002F0000-0001-8426-4158",{"name":208,"orcid":9},"Uchechukwu Nwafor",{"name":210,"orcid":9},"Anne Gobin",{"tldr":212,"method":213,"finding":214,"direction":78,"opportunity":215},"评估EO异常指标监测尼日利亚雨养水稻干旱影响，补充气象指数，提高产量预测。","结合NDVI和土壤水分指数异常、聚类阈值法、农户调查与回归模型。","EO指标（尤其SWIA）补充气象指数，显著提高干旱年份产量预测，关键物候期干旱影响最大。","可探索将EO异常与物候模型结合，开发针对小农的干旱预警系统，并验证其他作物和区域。","2026-09-08T23:30:18.761711Z",{"id":218,"title":219,"url":220,"summary":221,"summary_zh":222,"content":9,"source_name":223,"source_url":220,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":224,"score_detail":225,"sources":228,"tags":230,"search_phrases":233,"slug":236,"view_count":36,"doi":237,"paper":238,"created_at":249},1888,"Smart Technologies for Precision Nutrient Management in Smallholder Farming: A Comprehensive Review","https:\u002F\u002Fdoi.org\u002F10.4038\u002Fjur.v14i1.8134","Smallholder farmers with less than 2 hectares contribute more than 1\u002F3rd of the world food supply but continue to suffer the challenges of low nutrient use efficiency (30-40%), reduced soil fertility and lack of access to precision agriculture technologies. The aim of the review is to synthesize the evidence of 235 peer-reviewed articles (2010-2025) to assess the precision nutrient management (PNM) technologies, namely, in terms of their applicability, economic feasibility, and adoption capacity within the smallholder farming systems. Systematic literature review according to PRISMA, the analysis of articles on satellite and UAV-based remote sensing, IoT-enabled soil sensors, variable rate application systems, machine learning algorithms, and case studies worldwide were summarized. The application of variable rates can cut fertilizer use by 10-25 % and boost yields by 7-15 % but only small holders adopt it globally because of capital limitations, fragmented land holdings (1-2 ha), poor rural connectivity and insufficient extension services. Multi-source data forecasts nutrient needs with 85-92 % accuracy using machine learning algorithms (Random Forests, Support Vector Machines. Open-source sensor development, offline mobile decision support systems, equipment sharing cooperatives, blended finance (30–50% tech subsidies), and farmer involvement must all be part of the democratization of PNM for smallholders. Institutional adjustments, unified extension services, public-private cooperation, and supportive nutrient management regulations are all necessary for effective scaling. PNM adoption produces 15-30% nutrient runoff reduction, 10-20% greenhouse gas emission reductions, and 0.4-1.2 tons CO2-equivalent per hectare of soil carbon sequestration. PNM adoption produces 15-30% nutrient runoff reduction, 10-20% greenhouse gas emission reductions, and 0.4-1.2 tons CO2-equivalent per hectare of soil carbon sequestration. Economic analyses indicated that farms over 10 ha have positive returns, though smallholders required special support to overcome initial costs and limitations.","拥有不足2公顷土地的小农户贡献了全球粮食供应总量的三分之一以上，却持续面临养分利用效率低下（30-40%）、土壤肥力下降以及缺乏精准农业技术获取途径等挑战。本综述旨在综合235篇同行评审论文（2010-2025年）的证据，评估精准养分管理（PNM）技术在小农农业系统中的适用性、经济可行性和采纳能力。依据PRISMA方法进行系统性文献综述，总结了基于卫星和无人机遥感、物联网土壤传感器、变量施肥系统、机器学习算法及全球案例研究的分析结果。变量施肥技术的应用可减少化肥使用量10-25%，提高产量7-15%，但由于资金限制、土地碎片化（1-2公顷）、农村网络覆盖不足和推广服务欠缺，全球仅有少数小农户采纳该技术。利用机器学习算法（随机森林、支持向量机）进行多源数据预测，养分需求预测准确率可达85-92%。实现小农户PNM技术的普及化，必须包括开源传感器开发、离线移动决策支持系统、设备共享合作社、混合融资（30-50%技术补贴）以及农户参与等多方面措施。有效的规模化推广还需要制度调整、统一的推广服务、公私合作以及支持性养分管理法规的配套。PNM技术的采纳可减少养分径流15-30%，降低温室气体排放10-20%，并实现每公顷0.4-1.2吨二氧化碳当量的土壤碳封存。经济分析表明，10公顷以上的农场可获得正收益，但小农户需要特殊支持以克服初始成本和限制因素。","Journal of the University of Ruhuna",72,{"impact":18,"substance":93,"depth":18,"authority":175,"freshness":226,"relevant":22,"comment":227},2,"系统综述小农户精准养分管理技术，数据详实，但时效性低，适合专题参考。",[229],{"name":223,"url":220},[27,231,28,30,232],"精准施肥","小农户",[234,235],"农业遥感 智慧农业 机器学习 精准施肥","农业遥感 智慧农业","农业遥感智慧农业机器学习精准施肥-1888","10.4038\u002Fjur.v14i1.8134",{"doi":237,"openalex_id":239,"authors":240,"venue":223,"cited_by_count":36,"oa_url":220,"card":243,"direction":247,"ingested_from":80},"W7211915396",[241],{"name":242,"orcid":9},"Anuga Liyanage",{"tldr":244,"method":245,"finding":246,"direction":247,"opportunity":248},"综述小农户精准养分管理技术，分析其适用性、经济可行性与采纳障碍。","PRISMA系统综述235篇文献，分析遥感、物联网、变量施肥及机器学习案例。","变量施肥可减肥10-25%并增产7-15%，但小农户因资金、土地细碎等采纳率低。","智慧农业 \u002F 农业物联网","小农户精准养分管理技术民主化路径，如低成本传感器、离线决策支持系统及合作社共享模式。","2026-09-08T23:30:08.156887Z",{"id":251,"title":252,"url":253,"summary":254,"summary_zh":255,"content":9,"source_name":256,"source_url":253,"published_at":257,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":258,"score_detail":259,"sources":263,"tags":265,"search_phrases":267,"slug":270,"view_count":36,"doi":271,"paper":272,"created_at":292},1751,"Downscaling SMAP Soil Moisture to 1 km with Machine Learning and MODIS Data for Agricultural Drought Assessment in Békés County, Hungary","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8090373","Accurate mapping of Soil moisture (SM) is essential for effectively monitoring agricultural drought. However, the coarse spatial resolution of passive microwave products, including the 9 km Soil Moisture Active Passive (SMAP) retrievals, limits their effectiveness at regional and local scales. To address this limitation, three machine learning-based downscaling frameworks were compared to improve SMAP SM resolution from 9 km to 1 km over Békés County, Hungary. The study period covered the growing seasons (April to October) from 2020 to 2023. A set of multi-temporal MODIS-derived variables, including vegetation indices (NDVI, EVI), daytime and night-time land surface temperature, and evapotranspiration, along with land cover classification and topographic elevation, were combined as auxiliary predictor variables. Three machine learning algorithms, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Machine (GBM), were trained and evaluated. The results showed that (1) the RF model had the highest accuracy during the testing (R2 = 0.71, RMSE = 0.0295 m3\u002Fm3) phase and validation against four in situ monitoring stations with confirmed reliable SM estimation at the local scale; (2) daytime LST was the most important predictor in all models, underscoring the strong thermal–moisture coupling that governs surface SM dynamics; and (3) the validated RF model produced 1 km Standardized Soil Moisture Index (SSI) maps that effectively captured inter-annual drought variability, identifying the severe drought of July 2022. Overall, this study presents a downscaling approach for generating high-resolution SM data suitable for Central European agricultural environments. The resulting 1 km SM and SSI products provide valuable tools for decision-makers to enhance planning during drought periods and reduce agricultural losses through improved irrigation scheduling.","土壤水分（SM）的精确制图对于有效监测农业干旱至关重要。然而，被动微波产品（包括9公里分辨率的土壤水分主动被动（SMAP）反演数据）空间分辨率较粗，限制了其在区域和地方尺度上的有效性。为解决这一局限，本研究比较了三种基于机器学习的降尺度框架，旨在将匈牙利贝凯什县的SMAP土壤水分分辨率从9公里提升至1公里。研究时段覆盖2020年至2023年的生长季（4月至10月）。研究中将一组多时相MODIS衍生变量——包括植被指数（NDVI、EVI）、白天和夜间地表温度、蒸散量——以及土地覆盖分类和地形高程作为辅助预测变量。三种机器学习算法，即随机森林（RF）、极限梯度提升（XGBoost）和梯度提升机（GBM），被训练和评估。结果表明：（1）RF模型在测试阶段（R² = 0.71，RMSE = 0.0295 m³\u002Fm³）精度最高，且经四个原位监测站验证，确认其在小尺度上具有可靠的土壤水分估算能力；（2）白天地表温度是所有模型中最重要的预测变量，凸显了控制地表土壤水分动态的强烈热-湿耦合关系；（3）经过验证的RF模型生成的1公里标准化土壤水分指数（SSI）图有效捕捉了年际干旱变率，识别出2022年7月的严重干旱事件。总体而言，本研究提出了一种适用于中欧农业环境的降尺度方法，用于生成高分辨率土壤水分数据。所得到的1公里土壤水分和SSI产品为决策者在干旱期加强规划、通过改进灌溉调度减少农业损失提供了有价值的工具。","AgriEngineering","2026-09-04T00:00:00Z",66,{"impact":175,"substance":260,"depth":18,"authority":175,"freshness":261,"relevant":22,"comment":262},20,4,"研究利用机器学习将SMAP土壤水分降尺度至1km，提升农业干旱监测精度，方法新颖，数据详实，对区域农业管理有参考价值。",[264],{"name":256,"url":253},[27,28,30,181,266],"土壤水分",[268,269],"农业遥感 土壤水分 干旱监测 智慧农业","农业遥感 土壤水分","农业遥感土壤水分干旱监测智慧农业-1751","10.3390\u002Fagriengineering8090373",{"doi":271,"openalex_id":273,"authors":274,"venue":256,"cited_by_count":36,"oa_url":253,"card":286,"direction":291,"ingested_from":80},"W7208719644",[275,277,280,283],{"name":276,"orcid":9},"Mahrokh Shafiei",{"name":278,"orcid":279},"István Waltner","https:\u002F\u002Forcid.org\u002F0000-0002-6704-8936",{"name":281,"orcid":282},"Z. Vekerdy","https:\u002F\u002Forcid.org\u002F0000-0002-5677-8298",{"name":284,"orcid":285},"Gábor Halupka","https:\u002F\u002Forcid.org\u002F0000-0003-4640-0061",{"tldr":287,"method":288,"finding":289,"direction":78,"opportunity":290},"用机器学习将SMAP土壤水分降尺度至1km，评估匈牙利农业干旱。","比较RF、XGBoost、GBM，结合MODIS植被指数、地表温度等预测变量。","RF精度最高，白天LST最重要，成功捕捉2022年7月严重干旱。","可探索将降尺度方法应用于其他区域或作物，结合更高分辨率遥感或气象数据提升精度。","农业人工智能与决策模型","2026-09-05T23:30:50.054057Z",{"id":294,"title":295,"url":296,"summary":297,"summary_zh":298,"content":9,"source_name":299,"source_url":296,"published_at":300,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":301,"score_detail":302,"sources":304,"tags":306,"search_phrases":309,"slug":312,"view_count":36,"doi":313,"paper":314,"created_at":338},1532,"Analysis of crop residue cover in the North China Plain based on multispectral remote sensing and machine learning","https:\u002F\u002Fdoi.org\u002F10.1080\u002F01431161.2026.2727176","Crop residue cover (CRC) is a critical parameter for evaluating conservation tillage practices, mitigating soil erosion, and developing models for global carbon cycle monitoring. Traditional optical remote sensing index-based methods are significantly affected by variations in soil and crop residue moisture content as well as interference from emerging subsequent crops, resulting in low estimation accuracy and poor stability of CRC. Due to the presence of subsequent crops, the non-photosynthetic vegetation fraction (fNPV) derived from linear spectral mixture analysis is typically lower than the actual field CRC. This study proposes a novel CRC estimation method based on linear spectral mixture analysis. The workflow consists of three main components: (1) development of a Crop Residue Random Forest (CRRF) index using a random forest regression model to simulate the narrowband SINDRI index – which is less sensitive to moisture – from broadband multispectral reflectance; (2) construction of a two-dimensional triangular feature space combining the CRRF and NDVI indices, followed by linear spectral mixture analysis to simultaneously estimate field fNPV, photosynthetic vegetation fraction (fPV), and bare soil fraction (fBS); and (3) retrieval of the true CRC fraction at the sowing stage using the formula CRC = fNPV \u002F (1 − fPV). The proposed method was validated and applied regionally using multi-temporal Sentinel-2 MSI and MODIS imagery. Results demonstrate that: (1) the method effectively mitigates moisture interference and achieves high-accuracy fNPV estimation (R2 = 0.84, RMSE = 0.10); (2) the integration of Sentinel-2 and MODIS sensors enables stable estimation and spatio-temporal dynamic analysis of CRC across the North China Plain, with high temporal consistency between images acquired approximately 16 days apart (the majority of agricultural pixels showing absolute differences within ±0.15). This approach provides an efficient and robust technical solution for remote sensing monitoring of CRC fraction in complex agricultural environments and offers a valuable methodological reference for the assessment of conservation tillage practices.","作物残茬覆盖度（CRC）是评估保护性耕作措施、减缓土壤侵蚀以及构建全球碳循环监测模型的关键参数。传统光学遥感指数方法受土壤和作物残茬含水量变化及新生后茬作物干扰的影响显著，导致CRC估算精度低、稳定性差。由于后茬作物的存在，基于线性光谱混合分析提取的非光合植被覆盖度（fNPV）通常低于田间实际CRC值。本研究提出了一种基于线性光谱混合分析的CRC估算新方法。该方法流程主要包括三个部分：（1）利用随机森林回归模型模拟对水分敏感性较低的窄带SINDRI指数，进而构建作物残茬随机森林（CRRF）指数，该指数可由宽带多光谱反射率计算得到；（2）结合CRRF与NDVI指数构建二维三角特征空间，通过线性光谱混合分析同时估算田间fNPV、光合植被覆盖度（fPV）和裸土覆盖度（fBS）；（3）利用公式CRC = fNPV \u002F (1 − fPV)反演播种期真实CRC值。该方法利用多时相Sentinel-2 MSI和MODIS影像进行了验证及区域应用。结果表明：（1）该方法能有效削弱水分干扰，实现高精度fNPV估算（R² = 0.84，RMSE = 0.10）；（2）Sentinel-2与MODIS传感器的联合应用可实现华北平原CRC的稳定估算及时空动态分析，且间隔约16天获取的影像间具有较高时间一致性（绝大多数农业像元的绝对差异在±0.15以内）。该方法为复杂农业环境下CRC覆盖度的遥感监测提供了高效、稳健的技术方案，也为保护性耕作措施评估提供了有价值的方法参考。","International Journal of Remote Sensing","2026-09-02T00:00:00Z",76,{"impact":18,"substance":260,"depth":18,"authority":20,"freshness":176,"relevant":22,"comment":303},"提出基于随机森林与线性光谱混合分析的秸秆覆盖度估算新方法，在华北平原多源遥感数据上验证有效，对保护性耕作监测有方法论参考价值。",[305],{"name":299,"url":296},[27,28,30,307,308],"保护性耕作","秸秆覆盖",[310,311],"保护性耕作 农业遥感 智慧农业 机器学习","保护性耕作 农业遥感","保护性耕作农业遥感智慧农业机器学习-1532","10.1080\u002F01431161.2026.2727176",{"doi":313,"openalex_id":315,"authors":316,"venue":299,"cited_by_count":36,"oa_url":9,"card":333,"direction":78,"ingested_from":80},"W7205007595",[317,320,323,325,328,331],{"name":318,"orcid":319},"Jibo Yue","https:\u002F\u002Forcid.org\u002F0000-0001-9766-5313",{"name":321,"orcid":322},"Ranran Yang","https:\u002F\u002Forcid.org\u002F0000-0003-1937-2042",{"name":324,"orcid":9},"Yinghao Lin",{"name":326,"orcid":327},"Nianxu Xu","https:\u002F\u002Forcid.org\u002F0000-0002-0152-8750",{"name":329,"orcid":330},"Qingjiu Tian","https:\u002F\u002Forcid.org\u002F0000-0003-0986-6479",{"name":332,"orcid":9},"Jia Tian",{"tldr":334,"method":335,"finding":336,"direction":78,"opportunity":337},"提出基于线性光谱混合分析和随机森林的作物残茬覆盖度遥感估算方法，应用于华北平原。","随机森林模拟窄带SINDRI，结合NDVI构建三角特征空间，线性光谱混合分析估算","方法有效缓解水分干扰，fNPV估算R²=0.84，RMSE=0.10，多源影像时间一致性高。","可探索将方法扩展至其他作物类型或区域，或结合高光谱影像提高精度，并研究残茬覆盖度与土壤碳动态的关系。","2026-09-03T23:30:30.109393Z"]